Multiagent Planning and Learning for Smart Grid

We envision multiple autonomous intelligent agents working in concert to control the flow of electricity in the Smart Grid. The agents's actions are individually rational and also contribute to desirable global goals such as promoting the use of renewable energy, encouraging energy efficiency and enabling distributed fault tolerance.

Concretely we focus on the distribution portion of the grid where end-users and small-scale producers can participate directly in the market mechanisms of the grid. We assume liberalized markets where the only monopolies are the operators of the physical distribution infrastructure and the wholesale electricity market. The rest of the agents interact with the monopolies and each other through various cooperative or competitive relationships. The interactions may be bilateral contracts or intermediated through anonymous auctions. Many of the agents are required to make multiple simultaneous sequences of decisions that impact the agents themselves as well as their environment in multiple timescales.

We focus on planning and learning algorithms for various agents and on studying the equilibria that emerge from their interactions. We also study statistical machine learning techniques and game-theoretic methods for analyzing real-world and simulation data. Furthermore, we contribute to a large-scale open source simulation environment, PowerTAC, that facilitates these studies and also forms a reusable platform for other researchers to leverage our work.